基于全局特征和局部特征组合的脸谱识别方法

本文中,提出了一种新的脸谱识别方法。首先利用核主分量分析技术提取脸谱图象的全局特征,然后利用独立分量分析技术提取脸谱图象的局部特征,分别挑选出部分局部特征向量与部分全局特征向量组合成脸谱的组合特征向量,然后利用支持向量机分类器进行识别。采用ORL脸谱库进行测试,并与其它特征提取方法进行了比较,实验结果显示基于组合特征方法的识别率明显优于其它方法。

Face Recognition Method Based on Combination Features of Global and Local Features

In the paper, we propose a new method for face recognition. Firstly, we extract global features using Kernel Principal Component Analysis (KPCA)) technique and extract local features using Independent Component Analysis (ICA) technique. We select some of the local features and the global features and combine them. Then we performance classification using the combination features. For validation of our method, we have tested our method on the ORL face database by using linear Support Vector Machine. Meanwhile, we have compared performance of our method with that of PCA-based, KPCA-based and ICA-based methods. The experiment results show the performance of our method is superior to those of other methods.